def sfig1():
df = pd.merge(
add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(columns={"metric": "acw"}),
add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(columns={"metric": "acz"}),
on=["task", "subject", "region", "net_meta"], sort=False)
fig, axs = plt.subplots(2, 2, figsize=(15, 10), sharex=True, sharey="col")
ax = axs[0, 0]
sns.distplot(df.groupby(["task", "subject"]).mean().reset_index().acw, ax=ax, rug=False, kde=True, hist=True)
ax.set(xlabel=f"Normalized ACW-50 for subjects")
ax.grid(False)
ax = axs[1, 0]
sns.distplot(df.groupby(["task", "region"]).mean().reset_index().acw, ax=ax, rug=False, kde=True, hist=True)
ax.set(xlabel=f"Normalized ACW-50 for regions")
ax.grid(False)
ax = axs[0, 1]
sns.distplot(df.groupby(["task", "subject"]).mean().reset_index().acz, ax=ax, rug=False, kde=True, hist=True)
ax.grid(False)
ax.set(xlabel=f"Normalized ACW-0 for subjects")
ax = axs[1, 1]
sns.distplot(df.groupby(["task", "region"]).mean().reset_index().acz, ax=ax, rug=False, kde=True, hist=True)
ax.set(xlabel=f"Normalized ACW-0 for regions")
ax.grid(False)
fig.subplots_adjust(wspace=0.1, hspace=0.1)
savefig(fig, "sfig1.dist.nolabel")
def corr():
df = add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index()
df1 = add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index()
correlations = []
for task in task_order():
dft = and_filter(df, task=task)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
correlations.append(df_corr)
correlations1 = []
for task in task_order():
dft = and_filter(df1, task=task)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
correlations1.append(df_corr)
min_val, max_val = 0, 1
ticks = np.arange(min_val, max_val, 0.1)
cmap = cm.get_cmap("jet")
fig, axs = plt.subplots(2, 4, figsize=(20, 10))
for i, task in enumerate(task_order()):
ax = axs[0, i]
isc = correlations[i]
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
ax.set_title(f"ACW-50 {task}: {down:.2f}:{up:.2f}")
for i, task in enumerate(task_order()):
ax = axs[1, i]
isc = correlations1[i]
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
ax.set_title(f"ACW-0 {task}: {down:.2f}:{up:.2f}")
cbar_ax = fig.add_axes([0.92, 0.125, 0.02, 0.755])
cbar = fig.colorbar(pp, cax=cbar_ax, ticks=ticks, orientation="vertical")
savefig(fig, "isc", low=True)
def fig2():
unique_networks = net_order(tpt_name)
palette = make_net_palette(unique_networks)
dfs = [[], []]
for i, lib in enumerate([acw, acz]):
for avg in ["net_meta", "network"]:
df = and_filter(add_net_meta(lib.gen_long_data(tpt_name), get_net("pmc", tpt_name))
.groupby(["task", "subject", avg]).mean().reset_index(), task="Rest")
df.metric *= 1000
dfs[i].append(df)
fig = plt.figure(figsize=(15, 15))
gs = fig.add_gridspec(2, 2, width_ratios=[0.6, 2], hspace=0.1, wspace=0.2)
for row, ((df1, df2), label, (min_val, max_val)) in enumerate(
zip(dfs, ["ACW-50", "ACW-0"], [(28, 50), (150, 500)])):
ax = fig.add_subplot(gs[row, 0])
sns.barplot(data=df1, x="net_meta", y="metric", order=["P", "M", "C"], ax=ax)
ax.set(xlabel="", ylabel=f"Mean \u00B1 %95 CI (ms)", ylim=[min_val, max_val])
ax.set_xticklabels(PMC_labels if row == 1 else [], rotation=45)
ax = fig.add_subplot(gs[row, 1])
sns.barplot(data=df2, x="network", y="metric", order=unique_networks, palette=palette, ax=ax)
ax.set(xlabel="", ylabel="", ylim=[min_val, max_val], yticklabels=[])
ax.set_xticklabels(ax.get_xticklabels() if row == 1 else [], rotation=45)
ax.set_title(label, ha="center", loc="left", x=0.3)
savefig(fig, "fig2.bar")
def fig7():
df = pd.merge(
add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(columns={"metric": "acw"}),
add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(columns={"metric": "acz"}),
on=["task", "subject", "region", "net_meta"], sort=False)
fig, axs = plt.subplots(1, 2, figsize=(15, 10), sharex=True, sharey="col")
ax = axs[0, 0]
sns.distplot(df.acw, ax=ax, rug=False, kde=True, hist=True, kde_kws={"bw": 0.02})
ax.set(xlabel=f"Normalized ACW-50")
ax.grid(False)
ax = axs[1, 0]
for meta, label, color in zip(["P", "M", "C"], PMC_labels, PMC_colors):
sns.distplot(and_filter(df, net_meta=meta).acw, ax=ax, rug=False, kde=True, hist=False,
kde_kws={"bw": 0.02}, label=label, color=color)
ax.set(xlabel=f"Normalized ACW-50")
ax.grid(False)
ax = axs[0, 1]
sns.distplot(df.acz, ax=ax, rug=False, kde=True, hist=True, )
ax.grid(False)
ax.set(xlabel=f"Normalized ACW-0")
ax = axs[1, 1]
for meta, label, color in zip(["P", "M", "C"], PMC_labels, PMC_colors):
sns.distplot(and_filter(df, net_meta=meta).acz, ax=ax, rug=False, kde=True, hist=False,
label=label, color=color)
ax.set(xlabel=f"Normalized ACW-0")
ax.grid(False)
fig.subplots_adjust(wspace=0.1, hspace=0.1)
savefig(fig, "fig7.dist.nolabel")
def fig4():
unique_networks = net_order(tpt_name)
dfs = [[], []]
for i, lib in enumerate([acw, acz]):
for avg in ["net_meta", "network"]:
df = add_net_meta(
and_filter(lib.gen_long_data(tpt_name), subject=lib.find_shared_subjects(tpt_name, task_order())) \
.groupby(["task", "subject", "network", "region"]).mean().reset_index() \
.groupby(["subject", "network", "region"]).apply(calc_pchange).reset_index().drop("level_3", 1),
get_net("pmc", tpt_name)).groupby(["task", "subject", avg]).mean().reset_index()
df.pchange *= -1
dfs[i].append(df)
fig = plt.figure(figsize=(20, 15))
gs = fig.add_gridspec(2, 2, width_ratios=[0.7, 1.9], hspace=0.1, wspace=0.2)
for row, ((df1, df2), label, (min_val, max_val)) in enumerate(
zip(dfs, ["ACW-50", "ACW-0"], [(None, None), (None, None)])):
ax = fig.add_subplot(gs[row, 0])
sns.barplot(data=df1, x="task", y="pchange", hue="net_meta",
order=task_order(False), hue_order=["P", "M", "C"], ax=ax)
ax.set(xlabel="", ylabel=f"Mean \u00B1 %95 CI (% change)", ylim=[min_val, max_val])
if row == 0:
h, l = ax.get_legend_handles_labels()
ax.legend(h, PMC_labels, loc=3, ncol=3, mode="expand", borderaxespad=0, bbox_to_anchor=(0., 1.08, 1, 0.),
handletextpad=0.1)
else:
ax.get_legend().remove()
ax.set_xticklabels(ax.get_xticklabels() if row == 1 else [], rotation=45)
ax = fig.add_subplot(gs[row, 1])
sns.barplot(data=df2, x="network", y="pchange", hue="task", palette=task_colors,
hue_order=task_order(False), order=unique_networks, ax=ax)
ax.set(xlabel="", ylabel="", ylim=[min_val, max_val], yticklabels=[])
if row == 0:
lgn = ax.legend(loc=3, ncol=6, mode="expand", borderaxespad=0, bbox_to_anchor=(0., 1.08, 1, 0.))
else:
ax.get_legend().remove()
ax.set_xticklabels(ax.get_xticklabels() if row == 1 else [], rotation=45)
ax.set_title(label, ha="center", loc="left", x=0.3)
savefig(fig, "fig4.bar", extra_artists=(lgn,))
def kruskal():
for mes, mes_name in zip([acw, acz], ["acw", "acz"]):
for task in task_order(True):
for meta in ["net_meta", "network"]:
df = mes.gen_long_data(tpt_name)\
.and_filter(task=task)\
.add_net_meta(get_net("pmc", tpt_name))\
.groupby(["subject", meta]).mean().reset_index()\
.convert_column(metric=lambda x: x * 1000)
# feather.write_feather(df, f"r/{mes_name}.{task}.{meta}.feather")
model = ols(f'metric ~ C({meta})', data=df)
result = model.fit()
result.summary()
robust = None if result.diagn["omnipv"] > 0.05 else "hc3"
aov_table = anova_table(
sm.stats.anova_lm(result, typ=2, robust=robust))
print(aov_table.to_string())
if meta == "net_meta":
mc = MultiComparison(df.metric, df[meta])
mc_results = mc.tukeyhsd()
print(mc_results)
def regression():
df = pd.merge(
normalize(add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(
columns={"metric": "acw"}), "acw"),
normalize(add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index().rename(
columns={"metric": "acz"}), "acz"),
on=["task", "subject", "region", "net_meta"], sort=False)
df = and_filter(df, NOTnet_meta="M")
X = df.iloc[:, -2:].values
y = df.net_meta.map({"C": 0, "P": 1}).values
X = sm.add_constant(X)
model = sm.Logit(y, X)
result = model.fit()
print(result.summary())
mfx = result.get_margeff()
print(mfx.summary())
def isc():
df = and_filter(add_net_meta(normalize(acw.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index(), NOTnet_meta="M")
df1 = and_filter(add_net_meta(normalize(acz.gen_long_data(tpt_name), columns="metric"), get_net("pmc", tpt_name)) \
.groupby(["task", "subject", "region", "net_meta"]).mean().reset_index(), NOTnet_meta="M")
correlations = []
for task in task_order():
temp = []
for meta in ["C", "P"]:
dft = and_filter(df, task=task, net_meta=meta)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
temp.append(df_corr)
correlations.append(temp)
correlations1 = []
for task in task_order():
temp = []
for meta in ["C", "P"]:
dft = and_filter(df1, task=task, net_meta=meta)
subjects = dft.subject.unique()
df_corr = np.zeros((len(subjects), len(subjects)))
for i in range(len(subjects)):
df_corr[i, i] = 1
x = and_filter(dft, subject=subjects[i]).metric
for j in range(i + 1, len(subjects)):
y = and_filter(dft, subject=subjects[j]).metric
df_corr[i, j] = df_corr[j, i] = pearsonr(x, y)[0]
temp.append(df_corr)
correlations1.append(temp)
min_val, max_val = 0, 1
ticks = np.arange(min_val, max_val + 0.01, 0.1)
cmap = cm.get_cmap("jet")
fig, axs = plt.subplots(4, 4, figsize=(20, 20))
cbar_ax = fig.add_axes([0.92, 0.125, 0.02, 0.755])
for i, task in enumerate(task_order()):
for j, meta in enumerate(["Core", "Periphery"]):
ax = axs[j, i]
isc = correlations[i][j]
xy_ticks = np.linspace(1, len(isc), 10, dtype=np.int)
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.set(xticks=xy_ticks, yticks=xy_ticks)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
if j == 0:
ax.set_title(task, fontsize=18)
else:
ax.set_xticks([])
if i == 0:
ax.set_ylabel(meta, fontsize=18)
else:
ax.set_yticks([])
# ax.set_title(f"ACW-50: {down:.2f}:{up:.2f}")
for i, task in enumerate(task_order()):
for j, meta in enumerate(["Core", "Periphery"]):
ax = axs[j + 2, i]
isc = correlations1[i][j]
xy_ticks = np.linspace(1, len(isc), 10, dtype=np.int)
pp = ax.imshow(isc, interpolation="nearest", vmin=min_val, vmax=max_val, cmap=cmap)
ax.set(xticks=[], yticks=xy_ticks)
ax.xaxis.tick_top()
down, up = sms.DescrStatsW(isc[np.triu_indices(len(isc), 1)]).tconfint_mean()
if i == 0:
ax.set_ylabel(meta, fontsize=18)
else:
ax.set_yticks([])
# ax.set_title(f"ACW-0: {down:.2f}:{up:.2f}")
cbar = fig.colorbar(pp, cax=cbar_ax, ticks=ticks, orientation="vertical")
txt1 = fig.text(0.06, 0.67, "ACW-50", rotation=90, fontsize=18)
txt2 = fig.text(0.06, 0.27, "ACW-0", rotation=90, fontsize=18)
savefig(fig, "isc1", extra_artists=(txt1, txt2))